```r
#install.packages('TDAmapper')
library(TDAmapper)
library(cluster)
library(vip)
##
## Attaching package: 'vip'
## The following object is masked from 'package:utils':
##
## vi
#install.packages('kernlab’)
library(kernlab)
#install.packages(‘class’)
library(class)
#install.packages('nnet')
library(nnet)
#install.packages(‘randomForest’)
library(randomForest)
## randomForest 4.7-1.1
## Type rfNews() to see new features/changes/bug fixes.
#install.packages('e1071')
library(e1071)
#install.packages("BayesFactor")
library(BayesFactor)
## Loading required package: coda
##
## Attaching package: 'coda'
## The following object is masked from 'package:kernlab':
##
## nvar
## Loading required package: Matrix
## ************
## Welcome to BayesFactor 0.9.12-4.5. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
##
## Type BFManual() to open the manual.
## ************
library(BayesPPD)
library(bayestestR)
#install.packages('igraph')
library('igraph')
## Warning: package 'igraph' was built under R version 4.3.3
##
## Attaching package: 'igraph'
## The following object is masked from 'package:BayesFactor':
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## compare
## The following object is masked from 'package:class':
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## knn
## The following objects are masked from 'package:stats':
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## decompose, spectrum
## The following object is masked from 'package:base':
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## union
#install.packages('locfit')
library(locfit)
## locfit 1.5-9.8 2023-06-11
#install.packages('ggplot2’)
library(ggplot2)
##
## Attaching package: 'ggplot2'
## The following object is masked from 'package:randomForest':
##
## margin
## The following object is masked from 'package:kernlab':
##
## alpha
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:igraph':
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## as_data_frame, groups, union
## The following object is masked from 'package:randomForest':
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## combine
## The following objects are masked from 'package:stats':
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## filter, lag
## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union
#install.packages('networkD3')
library(networkD3)
library(rstanarm)
## Loading required package: Rcpp
## This is rstanarm version 2.26.1
## - See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
## - Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
## - For execution on a local, multicore CPU with excess RAM we recommend calling
## options(mc.cores = parallel::detectCores())
library(see)
#install.packages('tidyverse')
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.0
## ✔ readr 2.1.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ lubridate::%--%() masks igraph::%--%()
## ✖ ggplot2::alpha() masks kernlab::alpha()
## ✖ tibble::as_data_frame() masks dplyr::as_data_frame(), igraph::as_data_frame()
## ✖ dplyr::combine() masks randomForest::combine()
## ✖ purrr::compose() masks igraph::compose()
## ✖ purrr::cross() masks kernlab::cross()
## ✖ tidyr::crossing() masks igraph::crossing()
## ✖ tidyr::expand() masks Matrix::expand()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ ggplot2::margin() masks randomForest::margin()
## ✖ purrr::none() masks locfit::none()
## ✖ tidyr::pack() masks Matrix::pack()
## ✖ purrr::simplify() masks igraph::simplify()
## ✖ tidyr::unpack() masks Matrix::unpack()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
#install.packages('caret')
library(caret)
## Loading required package: lattice
##
## Attaching package: 'caret'
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## The following object is masked from 'package:purrr':
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## lift
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## compare_models, R2
#install.packages('ISLR')
library(ISLR)
#install.packages('MCMCpack')
library(MCMCpack)
## Loading required package: MASS
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
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## select
##
## ##
## ## Markov Chain Monte Carlo Package (MCMCpack)
## ## Copyright (C) 2003-2025 Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park
## ##
## ## Support provided by the U.S. National Science Foundation
## ## (Grants SES-0350646 and SES-0350613)
## ##
#linstall.packages("caret")
library(caret)
library(TDA)
##
## Attaching package: 'TDA'
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## The following object is masked from 'package:cluster':
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## silhouette
library(TDAstats)
library(ks)
##
## Attaching package: 'ks'
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## kde
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## The following object is masked from 'package:MCMCpack':
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## vech
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## compare
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## compare
#install.packages('MLmetrics')
library(MLmetrics)
##
## Attaching package: 'MLmetrics'
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## MAE, RMSE
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## Recall
#install.packages('googledrive')
library(googledrive)
#install.packages('stringr')
library(stringr)
#install.packages('ks')
library(ks)
library(GGally)
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
##Add Bayesian tests functions
#create function to conduct the Bayesian Sign Test
BayesianSignTest <- function(diffVector,rope_min,rope_max) {
library(MCMCpack)
samples <- 3000
#build the vector 0.5 1 1 ....... 1
weights <- c(0.5,rep(1,length(diffVector)))
#add the fake first observation in 0
diffVector <- c (0, diffVector)
#for the moment we implement the sign test. Signedrank will follows
probLeft <- mean (diffVector < rope_min)
probRope <- mean (diffVector > rope_min & diffVector < rope_max)
probRight <- mean (diffVector > rope_max)
results = list ("probLeft"=probLeft, "probRope"=probRope,
"probRight"=probRight)
return (results)
}
##Create function to conduct Bayesian Signed Rank Test
BayesianSignedRank <- function(diffVector,rope_min,rope_max) {
library(MCMCpack)
samples <- 30000
#build the vector 0.5 1 1 ....... 1
weights <- c(0.5,rep(1,length(diffVector)))
#add the fake first observation in 0
diffVector <- c (0, diffVector)
sampledWeights <- rdirichlet(samples,weights)
winLeft <- vector(length = samples)
winRope <- vector(length = samples)
winRight <- vector(length = samples)
for (rep in 1:samples){
currentWeights <- sampledWeights[rep,]
for (i in 1:length(currentWeights)){
for (j in 1:length(currentWeights)){
product= currentWeights[i] * currentWeights[j]
if (diffVector[i]+diffVector[j] > (2*rope_max) ) {
winRight[rep] <- winRight[rep] + product
}
else if (diffVector[i]+diffVector[j] > (2*rope_min) ) {
winRope[rep] <- winRope[rep] + product
}
else {
winLeft[rep] <- winLeft[rep] + product
}
}
}
maxWins=max(winRight[rep],winRope[rep],winLeft[rep])
winners = (winRight[rep]==maxWins)*1 + (winRope[rep]==maxWins)*1 + (winLeft[rep]==maxWins)*1
winRight[rep] <- (winRight[rep]==maxWins)*1/winners
winRope[rep] <- (winRope[rep]==maxWins)*1/winners
winLeft[rep] <- (winLeft[rep]==maxWins)*1/winners
}
results = list ("winLeft"=mean(winLeft), "winRope"=mean(winRope),
"winRight"=mean(winRight) )
return (results)
}
#Create function to conduct the Bayesian Correlated t.test
#diff_a_b is a vector of differences between the two classifiers, on each fold of cross-validation.
#If you have done 10 runs of 10-folds cross-validation, you have 100 results for each classifier.
#You should have run cross-validation on the same folds for the two classifiers.
#Then diff_a_b is the difference fold-by-fold.
#rho is the correlation of the cross-validation results: 1/(number of folds)
#rope_min and rope_max are the lower and the upper bound of the rope
correlatedBayesianTtest <- function(diff_a_b,rho,rope_min,rope_max){
if (rope_max < rope_min){
stop("rope_max should be larger than rope_min")
}
delta <- mean(diff_a_b)
n <- length(diff_a_b)
df <- n-1
stdX <- sd(diff_a_b)
sp <- sd(diff_a_b)*sqrt(1/n + rho/(1-rho))
p.left <- pt((rope_min - delta)/sp, df)
p.rope <- pt((rope_max - delta)/sp, df)-p.left
results <- list('left'=p.left,'rope'=p.rope,'right'=1-p.left-p.rope)
return (results)
}
set.seed(16974)
##Random Forest Results
rf_dataset_av<-c(0.8552, 0.9265562, 0.97876957)
rf_pca.5.50.5_n1_av<-c(0.9719, 0.9118504,0.99843287)
rf_pca.5.50.5_n2_av<-c(0.7323, 0.9020974,0.9847063)
rf_pca.5.50.5_n3_av<-c(0.8444, 0.93893757, 0.9847063)
rf_pca.5.50.5_n4_av<-c(0.9536, 0.97106917,0.9847063)
rf_pca.5.50.5_n5_av<-c(0.9983, 1,0.9847063)
rf_kde.5.50.5_n1_av<-c(0.8627, 0.951, 0.96649363)
rf_kde.5.50.5_n2_av<-c(0.8467, 0.944, 0.9786583)
rf_kde.5.50.5_n3_av<-c(0.8349, 0.913, 0.9840646)
rf_kde.5.50.5_n4_av<-c(0.8536, 0.820, 0.98788763)
rf_kde.5.50.5_n5_av<-c(0.8682, 0.729, 0.98885033)
######################## ROPE PCA
diff_rf_pca.5.50.5_n1_av<-rf_dataset_av - rf_pca.5.50.5_n1_av
bsr_diff_rf_pca.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.50.5_n1_av),-0.01,0.01)
bsr_diff_rf_pca.5.50.5_n1_av
## $winLeft
## [1] 0.6768
##
## $winRope
## [1] 0.2408667
##
## $winRight
## [1] 0.08233333
bsr_diff_rf_pca.5.50.5_n1_av_odds.left<-bsr_diff_rf_pca.5.50.5_n1_av $winLeft/bsr_diff_rf_pca.5.50.5_n1_av $winRight
bsr_diff_rf_pca.5.50.5_n1_av_odds.left
## [1] 8.220243
plot(rope(diff_rf_pca.5.50.5_n1_av,c(-0.01,0.01)))
diff_rf_pca.5.50.5_n2_av<-rf_dataset_av - rf_pca.5.50.5_n2_av
bsr_diff_rf_pca.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.50.5_n2_av),-0.01,0.01)
bsr_diff_rf_pca.5.50.5_n2_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.301
##
## $winRight
## [1] 0.699
bsr_diff_rf_pca.5.50.5_n2_av_odds.left<-bsr_diff_rf_pca.5.50.5_n2_av $winLeft/bsr_diff_rf_pca.5.50.5_n2_av $winRight
bsr_diff_rf_pca.5.50.5_n2_av_odds.left
## [1] 0
plot(rope(diff_rf_pca.5.50.5_n2_av,c(-0.01,0.01)))
diff_rf_pca.5.50.5_n3_av<-rf_dataset_av - rf_pca.5.50.5_n3_av
bsr_diff_rf_pca.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.50.5_n3_av),-0.01,0.01)
bsr_diff_rf_pca.5.50.5_n3_av
## $winLeft
## [1] 0.04913333
##
## $winRope
## [1] 0.9011333
##
## $winRight
## [1] 0.04973333
bsr_diff_rf_pca.5.50.5_n3_av_odds.left<-bsr_diff_rf_pca.5.50.5_n3_av $winLeft/bsr_diff_rf_pca.5.50.5_n3_av $winRight
bsr_diff_rf_pca.5.50.5_n3_av_odds.left
## [1] 0.9879357
plot(rope(diff_rf_pca.5.50.5_n3_av,c(-0.01,0.01)))
diff_rf_pca.5.50.5_n4_av<-rf_dataset_av - rf_pca.5.50.5_n4_av
bsr_diff_rf_pca.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.50.5_n4_av),-0.01,0.01)
bsr_diff_rf_pca.5.50.5_n4_av
## $winLeft
## [1] 0.8558667
##
## $winRope
## [1] 0.1441333
##
## $winRight
## [1] 0
bsr_diff_rf_pca.5.50.5_n4_av_odds.left<-bsr_diff_rf_pca.5.50.5_n4_av $winLeft/bsr_diff_rf_pca.5.50.5_n4_av $winRight
bsr_diff_rf_pca.5.50.5_n4_av_odds.left
## [1] Inf
plot(rope(diff_rf_pca.5.50.5_n4_av,c(-0.01,0.01)))
diff_rf_pca.5.50.5_n5_av<-rf_dataset_av - rf_pca.5.50.5_n5_av
bsr_diff_rf_pca.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.50.5_n5_av),-0.01,0.01)
bsr_diff_rf_pca.5.50.5_n5_av
## $winLeft
## [1] 0.8575667
##
## $winRope
## [1] 0.1424333
##
## $winRight
## [1] 0
bsr_diff_rf_pca.5.50.5_n5_av_odds.left<-bsr_diff_rf_pca.5.50.5_n5_av $winLeft/bsr_diff_rf_pca.5.50.5_n5_av $winRight
bsr_diff_rf_pca.5.50.5_n5_av_odds.left
## [1] Inf
plot(rope(diff_rf_pca.5.50.5_n5_av,c(-0.01,0.01)))
########################## ROPE KDE
diff_rf_kde.5.50.5_n1_av<-rf_dataset_av - rf_kde.5.50.5_n1_av
bsr_diff_rf_kde.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.50.5_n1_av),-0.01,0.01)
bsr_diff_rf_kde.5.50.5_n1_av
## $winLeft
## [1] 0.2614333
##
## $winRope
## [1] 0.684
##
## $winRight
## [1] 0.05456667
bsr_diff_rf_kde.5.50.5_n1_av_odds.left<-bsr_diff_rf_kde.5.50.5_n1_av$winLeft/bsr_diff_rf_kde.5.50.5_n1_av$winRight
bsr_diff_rf_kde.5.50.5_n1_av_odds.left
## [1] 4.791081
plot(rope(diff_rf_kde.5.50.5_n1_av,c(-0.01,0.01)))
diff_rf_kde.5.50.5_n2_av<-rf_dataset_av - rf_kde.5.50.5_n2_av
bsr_diff_rf_kde.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.50.5_n2_av),-0.01,0.01)
bsr_diff_rf_kde.5.50.5_n2_av
## $winLeft
## [1] 0.0473
##
## $winRope
## [1] 0.9527
##
## $winRight
## [1] 0
bsr_diff_rf_kde.5.50.5_n2_av_odds.left<-bsr_diff_rf_kde.5.50.5_n2_av$winLeft/bsr_diff_rf_kde.5.50.5_n2_av$winRight
bsr_diff_rf_kde.5.50.5_n2_av_odds.left
## [1] Inf
plot(rope(diff_rf_kde.5.50.5_n2_av,c(-0.01,0.01)))
diff_rf_kde.5.50.5_n3_av<-rf_dataset_av - rf_kde.5.50.5_n3_av
bsr_diff_rf_kde.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.50.5_n3_av),-0.01,0.01)
bsr_diff_rf_kde.5.50.5_n3_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.5823333
##
## $winRight
## [1] 0.4176667
bsr_diff_rf_kde.5.50.5_n3_av_odds.left<-bsr_diff_rf_kde.5.50.5_n3_av$winLeft/bsr_diff_rf_kde.5.50.5_n3_av$winRight
bsr_diff_rf_kde.5.50.5_n3_av_odds.left
## [1] 0
plot(rope(diff_rf_kde.5.50.5_n3_av,c(-0.01,0.01)))
diff_rf_kde.5.50.5_n4_av<-rf_dataset_av - rf_kde.5.50.5_n4_av
bsr_diff_rf_kde.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.50.5_n4_av),-0.01,0.01)
bsr_diff_rf_kde.5.50.5_n4_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.5781333
##
## $winRight
## [1] 0.4218667
bsr_diff_rf_kde.5.50.5_n4_av_odds.left<-bsr_diff_rf_kde.5.50.5_n4_av$winLeft/bsr_diff_rf_kde.5.50.5_n4_av$winRight
bsr_diff_rf_kde.5.50.5_n4_av_odds.left
## [1] 0
plot(rope(diff_rf_kde.5.50.5_n4_av,c(-0.01,0.01)))
diff_rf_kde.5.50.5_n5_av<-rf_dataset_av - rf_kde.5.50.5_n5_av
bsr_diff_rf_kde.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.50.5_n5_av),-0.01,0.01)
bsr_diff_rf_kde.5.50.5_n5_av
## $winLeft
## [1] 0.3848667
##
## $winRope
## [1] 0.1318667
##
## $winRight
## [1] 0.4832667
bsr_diff_rf_kde.5.50.5_n5_av_odds.left<-bsr_diff_rf_kde.5.50.5_n5_av$winLeft/bsr_diff_rf_kde.5.50.5_n5_av$winRight
bsr_diff_rf_kde.5.50.5_n5_av_odds.left
## [1] 0.7963857
plot(rope(diff_rf_kde.5.50.5_n5_av,c(-0.01,0.01)))
################################ Support Vector Machine
##Support Vector Machine Results
svm_dataset_av<-c(0.8204, 0.929, 0.97677423)
svm_pca.5.50.5_n1_av<-c(0.6973, 0.915, 0.998432867)
svm_pca.5.50.5_n2_av<-c(0.6970, 0.645, 0.9837314)
svm_pca.5.50.5_n3_av<-c(0.8014, 0.947, 0.9199994)
svm_pca.5.50.5_n4_av<-c(0.9460, 0.981, 0.958260767)
svm_pca.5.50.5_n5_av<-c(0.9980, 1.000, 1.00)
svm_kde.5.50.5_n1_av<-c(0.8147, 0.955, 0.988035433)
svm_kde.5.50.5_n2_av<-c(0.8076, 0.947, 0.980024)
svm_kde.5.50.5_n3_av<-c(0.8026, 0.918, 0.985011133)
svm_kde.5.50.5_n4_av<-c(0.8388, 0.820, 0.988626333)
svm_kde.5.50.5_n5_av<-c(0.8051, 0.754, 0.989235033)
######################## ROPE PCA
diff_svm_pca.5.50.5_n1_av<-svm_dataset_av - svm_pca.5.50.5_n1_av
bsr_diff_svm_pca.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.50.5_n1_av),-0.01,0.01)
bsr_diff_svm_pca.5.50.5_n1_av
## $winLeft
## [1] 0.1495
##
## $winRope
## [1] 0.1502333
##
## $winRight
## [1] 0.7002667
bsr_diff_svm_pca.5.50.5_n1_av_odds.left<-bsr_diff_svm_pca.5.50.5_n1_av$winLeft/bsr_diff_svm_pca.5.50.5_n1_av $winRight
bsr_diff_svm_pca.5.50.5_n1_av_odds.left
## [1] 0.2134901
plot(rope(diff_svm_pca.5.50.5_n1_av,c(-0.01,0.01)))
diff_svm_pca.5.50.5_n2_av<-svm_dataset_av - svm_pca.5.50.5_n2_av
bsr_diff_svm_pca.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.50.5_n2_av),-0.01,0.01)
bsr_diff_svm_pca.5.50.5_n2_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1438
##
## $winRight
## [1] 0.8562
bsr_diff_svm_pca.5.50.5_n2_av_odds.left<-bsr_diff_svm_pca.5.50.5_n2_av$winLeft/bsr_diff_svm_pca.5.50.5_n1_av$winRight
bsr_diff_svm_pca.5.50.5_n2_av_odds.left
## [1] 0
plot(rope(diff_svm_pca.5.50.5_n2_av,c(-0.01,0.01)))
diff_svm_pca.5.50.5_n3_av<-svm_dataset_av - svm_pca.5.50.5_n3_av
bsr_diff_svm_pca.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.50.5_n3_av),-0.01,0.01)
bsr_diff_svm_pca.5.50.5_n3_av
## $winLeft
## [1] 0.07836667
##
## $winRope
## [1] 0.2436667
##
## $winRight
## [1] 0.6779667
bsr_diff_svm_pca.5.50.5_n3_av_odds.left<-bsr_diff_svm_pca.5.50.5_n3_av$winLeft/bsr_diff_svm_pca.5.50.5_n3_av$winRight
bsr_diff_svm_pca.5.50.5_n3_av_odds.left
## [1] 0.1155907
plot(rope(diff_svm_pca.5.50.5_n3_av,c(-0.01,0.01)))
diff_svm_pca.5.50.5_n4_av<-svm_dataset_av - svm_pca.5.50.5_n4_av
bsr_diff_svm_pca.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.50.5_n4_av),-0.01,0.01)
bsr_diff_svm_pca.5.50.5_n4_av
## $winLeft
## [1] 0.8869333
##
## $winRope
## [1] 0.05153333
##
## $winRight
## [1] 0.06153333
bsr_diff_svm_pca.5.50.5_n4_av_odds.left<-bsr_diff_svm_pca.5.50.5_n4_av$winLeft/bsr_diff_svm_pca.5.50.5_n4_av$winRight
bsr_diff_svm_pca.5.50.5_n4_av_odds.left
## [1] 14.41387
plot(rope(diff_svm_pca.5.50.5_n4_av,c(-0.01,0.01)))
diff_svm_pca.5.50.5_n5_av<-svm_dataset_av - svm_pca.5.50.5_n5_av
bsr_diff_svm_pca.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.50.5_n5_av),-0.01,0.01)
bsr_diff_svm_pca.5.50.5_n5_av
## $winLeft
## [1] 0.9919
##
## $winRope
## [1] 0.0081
##
## $winRight
## [1] 0
bsr_diff_svm_pca.5.50.5_n5_av_odds.left<-bsr_diff_svm_pca.5.50.5_n5_av$winLeft/bsr_diff_svm_pca.5.50.5_n5_av$winRight
bsr_diff_svm_pca.5.50.5_n5_av_odds.left
## [1] Inf
plot(rope(diff_svm_pca.5.50.5_n5_av,c(-0.01,0.01)))
########################## ROPE KDE
diff_svm_kde.5.50.5_n1_av<-svm_dataset_av - svm_kde.5.50.5_n1_av
bsr_diff_svm_kde.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.50.5_n1_av),-0.01,0.01)
bsr_diff_svm_kde.5.50.5_n1_av
## $winLeft
## [1] 0.6059667
##
## $winRope
## [1] 0.3940333
##
## $winRight
## [1] 0
bsr_diff_svm_kde.5.50.5_n1_av_odds.left<-bsr_diff_svm_kde.5.50.5_n1_av$winLeft/bsr_diff_svm_kde.5.50.5_n1_av$winRight
bsr_diff_svm_kde.5.50.5_n1_av_odds.left
## [1] Inf
plot(rope(diff_svm_kde.5.50.5_n1_av,c(-0.01,0.01)))
diff_svm_kde.5.50.5_n2_av<-svm_dataset_av - svm_kde.5.50.5_n2_av
bsr_diff_svm_kde.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.50.5_n2_av),-0.01,0.01)
bsr_diff_svm_kde.5.50.5_n2_av
## $winLeft
## [1] 0.1687667
##
## $winRope
## [1] 0.7766333
##
## $winRight
## [1] 0.0546
bsr_diff_svm_kde.5.50.5_n2_av_odds.left<-bsr_diff_svm_kde.5.50.5_n2_av$winLeft/bsr_diff_svm_kde.5.50.5_n2_av$winRight
bsr_diff_svm_kde.5.50.5_n2_av_odds.left
## [1] 3.090965
plot(rope(diff_svm_kde.5.50.5_n2_av,c(-0.01,0.01)))
diff_svm_kde.5.50.5_n3_av<-svm_dataset_av - svm_kde.5.50.5_n3_av
bsr_diff_svm_kde.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.50.5_n3_av),-0.01,0.01)
bsr_diff_svm_kde.5.50.5_n3_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.6716333
##
## $winRight
## [1] 0.3283667
bsr_diff_svm_kde.5.50.5_n3_av_odds.left<-bsr_diff_svm_kde.5.50.5_n3_av$winLeft/bsr_diff_svm_kde.5.50.5_n3_av$winRight
bsr_diff_svm_kde.5.50.5_n3_av_odds.left
## [1] 0
plot(rope(diff_svm_kde.5.50.5_n3_av,c(-0.01,0.01)))
diff_svm_kde.5.50.5_n4_av<-svm_dataset_av - svm_kde.5.50.5_n4_av
bsr_diff_svm_kde.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.50.5_n4_av),-0.01,0.01)
bsr_diff_svm_kde.5.50.5_n4_av
## $winLeft
## [1] 0.3897
##
## $winRope
## [1] 0.1306333
##
## $winRight
## [1] 0.4796667
bsr_diff_svm_kde.5.50.5_n4_av_odds.left<-bsr_diff_svm_kde.5.50.5_n4_av$winLeft/bsr_diff_svm_kde.5.50.5_n4_av$winRight
bsr_diff_svm_kde.5.50.5_n4_av_odds.left
## [1] 0.8124392
plot(rope(diff_svm_kde.5.50.5_n4_av,c(-0.01,0.01)))
diff_svm_kde.5.50.5_n5_av<-svm_dataset_av - svm_kde.5.50.5_n5_av
bsr_diff_svm_kde.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.50.5_n5_av),-0.01,0.01)
bsr_diff_svm_kde.5.50.5_n5_av
## $winLeft
## [1] 0.07643333
##
## $winRope
## [1] 0.2448667
##
## $winRight
## [1] 0.6787
bsr_diff_svm_kde.5.50.5_n5_av_odds.left<-bsr_diff_svm_kde.5.50.5_n5_av$winLeft/bsr_diff_svm_kde.5.50.5_n5_av$winRight
bsr_diff_svm_kde.5.50.5_n5_av_odds.left
## [1] 0.1126173
plot(rope(diff_svm_kde.5.50.5_n5_av,c(-0.01,0.01)))
######################### Neural Network
##Neural Network Results
nn1_dataset_av<-c(0.8295538, 0.602, 0.97852987)
nn1_pca.5.50.5_n1_av<-c(0.97335787, 0.864, 0.99843287)
nn1_pca.5.50.5_n2_av<-c(0.69048193, 0.468, 0.98379247)
nn1_pca.5.50.5_n3_av<-c(0.82212793, 0.856, 0.911938)
nn1_pca.5.50.5_n4_av<-c(0.9531738, 0.862, 0.96000143)
nn1_pca.5.50.5_n5_av<-c(0.99798667, 0.998, 0.66823899)
nn1_kde.5.50.5_n1_av<-c(0.5557, 0.572, 0.967122867)
nn1_kde.5.50.5_n2_av<-c(0.8085, 0.746, 0.9856422)
nn1_kde.5.50.5_n3_av<-c(0.8093, 0.876, 0.985484367)
nn1_kde.5.50.5_n4_av<-c(0.8354, 0.788, 0.988035433)
nn1_kde.5.50.5_n5_av<-c(0.8686, 0.740, 0.989234933)
######################## ROPE PCA
diff_nn1_pca.5.50.5_n1_av<-nn1_dataset_av - nn1_pca.5.50.5_n1_av
bsr_diff_nn1_pca.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.50.5_n1_av),-0.01,0.01)
bsr_diff_nn1_pca.5.50.5_n1_av
## $winLeft
## [1] 0.9632
##
## $winRope
## [1] 0.0368
##
## $winRight
## [1] 0
bsr_diff_nn1_pca.5.50.5_n1_av_odds.left<-bsr_diff_nn1_pca.5.50.5_n1_av$winLeft/bsr_diff_nn1_pca.5.50.5_n1_av$winRight
bsr_diff_nn1_pca.5.50.5_n1_av_odds.left
## [1] Inf
plot(rope(diff_nn1_pca.5.50.5_n1_av,c(-0.01,0.01)))
diff_nn1_pca.5.50.5_n2_av<-nn1_dataset_av - nn1_pca.5.50.5_n2_av
bsr_diff_nn1_pca.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.50.5_n2_av),-0.01,0.01)
bsr_diff_nn1_pca.5.50.5_n2_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1440333
##
## $winRight
## [1] 0.8559667
bsr_diff_nn1_pca.5.50.5_n2_av_odds.left<-bsr_diff_nn1_pca.5.50.5_n2_av$winLeft/bsr_diff_nn1_pca.5.50.5_n2_av$winRight
bsr_diff_nn1_pca.5.50.5_n2_av_odds.left
## [1] 0
plot(rope(diff_nn1_pca.5.50.5_n2_av,c(-0.01,0.01)))
diff_nn1_pca.5.50.5_n3_av<-nn1_dataset_av - nn1_pca.5.50.5_n3_av
bsr_diff_nn1_pca.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.50.5_n3_av),-0.01,0.01)
bsr_diff_nn1_pca.5.50.5_n3_av
## $winLeft
## [1] 0.4963667
##
## $winRope
## [1] 0.1999667
##
## $winRight
## [1] 0.3036667
bsr_diff_nn1_pca.5.50.5_n3_av_odds.left<-bsr_diff_nn1_pca.5.50.5_n3_av$winLeft/bsr_diff_nn1_pca.5.50.5_n3_av$winRight
bsr_diff_nn1_pca.5.50.5_n3_av_odds.left
## [1] 1.634577
plot(rope(diff_nn1_pca.5.50.5_n3_av,c(-0.01,0.01)))
diff_nn1_pca.5.50.5_n4_av<-nn1_dataset_av - nn1_pca.5.50.5_n4_av
bsr_diff_nn1_pca.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.50.5_n4_av),-0.01,0.01)
bsr_diff_nn1_pca.5.50.5_n4_av
## $winLeft
## [1] 0.8885
##
## $winRope
## [1] 0.05096667
##
## $winRight
## [1] 0.06053333
bsr_diff_nn1_pca.5.50.5_n4_av_odds.left<-bsr_diff_nn1_pca.5.50.5_n4_av$winLeft/bsr_diff_nn1_pca.5.50.5_n4_av$winRight
bsr_diff_nn1_pca.5.50.5_n4_av_odds.left
## [1] 14.67786
plot(rope(diff_nn1_pca.5.50.5_n4_av,c(-0.01,0.01)))
diff_nn1_pca.5.50.5_n5_av<-nn1_dataset_av - nn1_pca.5.50.5_n5_av
bsr_diff_nn1_pca.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.50.5_n5_av),-0.01,0.01)
bsr_diff_nn1_pca.5.50.5_n5_av
## $winLeft
## [1] 0.7272667
##
## $winRope
## [1] 0.01633333
##
## $winRight
## [1] 0.2564
bsr_diff_nn1_pca.5.50.5_n5_av_odds.left<-bsr_diff_nn1_pca.5.50.5_n5_av$winLeft/bsr_diff_nn1_pca.5.50.5_n5_av$winRight
bsr_diff_nn1_pca.5.50.5_n5_av_odds.left
## [1] 2.836453
plot(rope(diff_nn1_pca.5.50.5_n5_av,c(-0.01,0.01)))
########################## ROPE KDE
diff_nn1_kde.5.50.5_n1_av<-nn1_dataset_av - nn1_kde.5.50.5_n1_av
bsr_diff_nn1_kde.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.50.5_n1_av),-0.01,0.01)
bsr_diff_nn1_kde.5.50.5_n1_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.03696667
##
## $winRight
## [1] 0.9630333
bsr_diff_nn1_kde.5.50.5_n1_av_odds.left<-bsr_diff_nn1_kde.5.50.5_n1_av $winLeft/bsr_diff_nn1_kde.5.50.5_n1_av $winRight
bsr_diff_nn1_kde.5.50.5_n1_av_odds.left
## [1] 0
plot(rope(diff_nn1_kde.5.50.5_n1_av,c(-0.01,0.01)))
diff_nn1_kde.5.50.5_n2_av<-nn1_dataset_av - nn1_kde.5.50.5_n2_av
bsr_diff_nn1_kde.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.50.5_n2_av),-0.01,0.01)
bsr_diff_nn1_kde.5.50.5_n2_av
## $winLeft
## [1] 0.4899
##
## $winRope
## [1] 0.3676333
##
## $winRight
## [1] 0.1424667
bsr_diff_nn1_kde.5.50.5_n2_av_odds.left<-bsr_diff_nn1_kde.5.50.5_n2_av$winLeft/bsr_diff_nn1_kde.5.50.5_n2_av$winRight
bsr_diff_nn1_kde.5.50.5_n2_av_odds.left
## [1] 3.438699
plot(rope(diff_nn1_kde.5.50.5_n2_av,c(-0.01,0.01)))
diff_nn1_kde.5.50.5_n3_av<-nn1_dataset_av - nn1_kde.5.50.5_n3_av
bsr_diff_nn1_kde.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.50.5_n3_av),-0.01,0.01)
bsr_diff_nn1_kde.5.50.5_n3_av
## $winLeft
## [1] 0.4949667
##
## $winRope
## [1] 0.3646667
##
## $winRight
## [1] 0.1403667
bsr_diff_nn1_kde.5.50.5_n3_av_odds.left<-bsr_diff_nn1_kde.5.50.5_n3_av$winLeft/bsr_diff_nn1_kde.5.50.5_n3_av$winRight
bsr_diff_nn1_kde.5.50.5_n3_av_odds.left
## [1] 3.526241
plot(rope(diff_nn1_kde.5.50.5_n3_av,c(-0.01,0.01)))
diff_nn1_kde.5.50.5_n4_av<-nn1_dataset_av - nn1_kde.5.50.5_n4_av
bsr_diff_nn1_kde.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.50.5_n4_av),-0.01,0.01)
bsr_diff_nn1_kde.5.50.5_n4_av
## $winLeft
## [1] 0.4213333
##
## $winRope
## [1] 0.5786667
##
## $winRight
## [1] 0
bsr_diff_nn1_kde.5.50.5_n4_av_odds.left<-bsr_diff_nn1_kde.5.50.5_n4_av$winLeft/bsr_diff_nn1_kde.5.50.5_n4_av$winRight
bsr_diff_nn1_kde.5.50.5_n4_av_odds.left
## [1] Inf
plot(rope(diff_nn1_kde.5.50.5_n4_av,c(-0.01,0.01)))
diff_nn1_kde.5.50.5_n5_av<-nn1_dataset_av - nn1_kde.5.50.5_n5_av
bsr_diff_nn1_kde.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.50.5_n5_av),-0.01,0.01)
bsr_diff_nn1_kde.5.50.5_n5_av
## $winLeft
## [1] 0.9623333
##
## $winRope
## [1] 0.03766667
##
## $winRight
## [1] 0
bsr_diff_nn1_kde.5.50.5_n5_av_odds.left<-bsr_diff_nn1_kde.5.50.5_n5_av$winLeft/bsr_diff_nn1_kde.5.50.5_n5_av$winRight
bsr_diff_nn1_kde.5.50.5_n5_av_odds.left
## [1] Inf
plot(rope(diff_nn1_kde.5.50.5_n5_av,c(-0.01,0.01)))
################################ Logistic Regression
##Logistic Regression Results
lr_dataset_av<-c(0.8486, 0.927, 0.97749227)
lr_pca.5.50.5_n1_av<-c(0.8954, 0.910, 0.99843287)
lr_pca.5.50.5_n2_av<-c(0.7171, 0.900, 0.9833048)
lr_pca.5.50.5_n3_av<-c(0.8289, 0.945, 0.89953653)
lr_pca.5.50.5_n4_av<-c(0.9485, 0.984, 0.9599969)
lr_pca.5.50.5_n5_av<-c(0.9338, 1.000, 0.66665922)
lr_kde.5.50.5_n1_av<-c(0.953, 0.572, 0.964291333)
lr_kde.5.50.5_n2_av<-c(0.947, 0.746, 0.980023833)
lr_kde.5.50.5_n3_av<-c(0.917, 0.876, 0.9862735)
lr_kde.5.50.5_n4_av<-c(0.827, 0.788, 0.9884785)
lr_kde.5.50.5_n5_av<-c(0.617, 0.740, 0.9890427)
######################## ROPE PCA
diff_lr_pca.5.50.5_n1_av<-lr_dataset_av - lr_pca.5.50.5_n1_av
bsr_diff_lr_pca.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.50.5_n1_av),-0.01,0.01)
bsr_diff_lr_pca.5.50.5_n1_av
## $winLeft
## [1] 0.7772667
##
## $winRope
## [1] 0.1393667
##
## $winRight
## [1] 0.08336667
bsr_diff_lr_pca.5.50.5_n1_av_odds.left<-bsr_diff_lr_pca.5.50.5_n1_av$winLeft/bsr_diff_lr_pca.5.50.5_n1_av$winRight
bsr_diff_lr_pca.5.50.5_n1_av_odds.left
## [1] 9.323471
plot(rope(diff_lr_pca.5.50.5_n1_av,c(-0.01,0.01)))
diff_lr_pca.5.50.5_n2_av<-lr_dataset_av - lr_pca.5.50.5_n2_av
bsr_diff_lr_pca.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.50.5_n2_av),-0.01,0.01)
bsr_diff_lr_pca.5.50.5_n2_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1459333
##
## $winRight
## [1] 0.8540667
bsr_diff_lr_pca.5.50.5_n2_av_odds.left<-bsr_diff_lr_pca.5.50.5_n2_av$winLeft/bsr_diff_lr_pca.5.50.5_n2_av$winRight
bsr_diff_lr_pca.5.50.5_n2_av_odds.left
## [1] 0
plot(rope(diff_lr_pca.5.50.5_n2_av,c(-0.01,0.01)))
diff_lr_pca.5.50.5_n3_av<-lr_dataset_av - lr_pca.5.50.5_n3_av
bsr_diff_lr_pca.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.50.5_n3_av),-0.01,0.01)
bsr_diff_lr_pca.5.50.5_n3_av
## $winLeft
## [1] 0.07876667
##
## $winRope
## [1] 0.2441333
##
## $winRight
## [1] 0.6771
bsr_diff_lr_pca.5.50.5_n3_av_odds.left<-bsr_diff_lr_pca.5.50.5_n3_av$winLeft/bsr_diff_lr_pca.5.50.5_n3_av$winRight
bsr_diff_lr_pca.5.50.5_n3_av_odds.left
## [1] 0.1163294
plot(rope(diff_lr_pca.5.50.5_n3_av,c(-0.01,0.01)))
diff_lr_pca.5.50.5_n4_av<-lr_dataset_av - lr_pca.5.50.5_n4_av
bsr_diff_lr_pca.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.50.5_n4_av),-0.01,0.01)
bsr_diff_lr_pca.5.50.5_n4_av
## $winLeft
## [1] 0.8857
##
## $winRope
## [1] 0.0525
##
## $winRight
## [1] 0.0618
bsr_diff_lr_pca.5.50.5_n4_av_odds.left<-bsr_diff_lr_pca.5.50.5_n4_av$winLeft/bsr_diff_lr_pca.5.50.5_n4_av$winRight
bsr_diff_lr_pca.5.50.5_n4_av_odds.left
## [1] 14.33172
plot(rope(diff_lr_pca.5.50.5_n4_av,c(-0.01,0.01)))
diff_lr_pca.5.50.5_n5_av<-lr_dataset_av - lr_pca.5.50.5_n5_av
bsr_diff_lr_pca.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.50.5_n5_av),-0.01,0.01)
bsr_diff_lr_pca.5.50.5_n5_av
## $winLeft
## [1] 0.5425333
##
## $winRope
## [1] 0.0159
##
## $winRight
## [1] 0.4415667
bsr_diff_lr_pca.5.50.5_n5_av_odds.left<-bsr_diff_lr_pca.5.50.5_n5_av$winLeft/bsr_diff_lr_pca.5.50.5_n5_av$winRight
bsr_diff_lr_pca.5.50.5_n5_av_odds.left
## [1] 1.228656
plot(rope(diff_lr_pca.5.50.5_n5_av,c(-0.01,0.01)))
########################## ROPE KDE
diff_lr_kde.5.50.5_n1_av<-lr_dataset_av - lr_kde.5.50.5_n1_av
bsr_diff_lr_kde.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.50.5_n1_av),-0.01,0.01)
bsr_diff_lr_kde.5.50.5_n1_av
## $winLeft
## [1] 0.2802
##
## $winRope
## [1] 0.05953333
##
## $winRight
## [1] 0.6602667
bsr_diff_lr_kde.5.50.5_n1_av_odds.left<-bsr_diff_lr_kde.5.50.5_n1_av $winLeft/bsr_diff_lr_kde.5.50.5_n1_av$winRight
bsr_diff_lr_kde.5.50.5_n1_av_odds.left
## [1] 0.424374
plot(rope(diff_lr_kde.5.50.5_n1_av,c(-0.01,0.01)))
diff_lr_kde.5.50.5_n2_av<-lr_dataset_av - lr_kde.5.50.5_n2_av
bsr_diff_lr_kde.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.50.5_n2_av),-0.01,0.01)
bsr_diff_lr_kde.5.50.5_n2_av
## $winLeft
## [1] 0.3011
##
## $winRope
## [1] 0.2008333
##
## $winRight
## [1] 0.4980667
bsr_diff_lr_kde.5.50.5_n2_av_odds.left<-bsr_diff_lr_kde.5.50.5_n2_av $winLeft/bsr_diff_lr_kde.5.50.5_n2_av$winRight
bsr_diff_lr_kde.5.50.5_n2_av_odds.left
## [1] 0.6045375
plot(rope(diff_lr_kde.5.50.5_n2_av,c(-0.01,0.01)))
diff_lr_kde.5.50.5_n3_av<-lr_dataset_av - lr_kde.5.50.5_n3_av
bsr_diff_lr_kde.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.50.5_n3_av),-0.01,0.01)
bsr_diff_lr_kde.5.50.5_n3_av
## $winLeft
## [1] 0.3464333
##
## $winRope
## [1] 0.3018
##
## $winRight
## [1] 0.3517667
bsr_diff_lr_kde.5.50.5_n3_av_odds.left<-bsr_diff_lr_kde.5.50.5_n3_av $winLeft/bsr_diff_lr_kde.5.50.5_n3_av$winRight
bsr_diff_lr_kde.5.50.5_n3_av_odds.left
## [1] 0.9848384
plot(rope(diff_lr_kde.5.50.5_n3_av,c(-0.01,0.01)))
diff_lr_kde.5.50.5_n4_av<-lr_dataset_av - lr_kde.5.50.5_n4_av
bsr_diff_lr_kde.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.50.5_n4_av),-0.01,0.01)
bsr_diff_lr_kde.5.50.5_n4_av
## $winLeft
## [1] 0.0794
##
## $winRope
## [1] 0.1433333
##
## $winRight
## [1] 0.7772667
bsr_diff_lr_kde.5.50.5_n4_av_odds.left<-bsr_diff_lr_kde.5.50.5_n4_av $winLeft/bsr_diff_lr_kde.5.50.5_n4_av$winRight
bsr_diff_lr_kde.5.50.5_n4_av_odds.left
## [1] 0.1021528
plot(rope(diff_lr_kde.5.50.5_n4_av,c(-0.01,0.01)))
diff_lr_kde.5.50.5_n5_av<-lr_dataset_av - lr_kde.5.50.5_n5_av
bsr_diff_lr_kde.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.50.5_n5_av),-0.01,0.01)
bsr_diff_lr_kde.5.50.5_n5_av
## $winLeft
## [1] 0.0634
##
## $winRope
## [1] 0.0513
##
## $winRight
## [1] 0.8853
bsr_diff_lr_kde.5.50.5_n5_av_odds.left<-bsr_diff_lr_kde.5.50.5_n5_av $winLeft/bsr_diff_lr_kde.5.50.5_n5_av$winRight
bsr_diff_lr_kde.5.50.5_n5_av_odds.left
## [1] 0.07161414
plot(rope(diff_lr_kde.5.50.5_n5_av,c(-0.01,0.01)))
#################################################### Naive Bayes
##Naive Bayes Results
nb_dataset_av<-c(0.76203217, 0.90190013, 0.9592941)
nb_pca.5.50.5_n1_av<-c(0.97335777, 0.85852737, 0.9784864)
#nb_pca.5.50.5_n2_av<-c(0.5561222, NA, 0.95338763)
#nb_pca.5.50.5_n3_av<-c(0.7714502, NA, 0.89953653)
nb_pca.5.50.5_n4_av<-c(0.95786153, 0.984, 0.8939295)
#nb_pca.5.50.5_n5_av<-c(0.99798667, NA, NA)
#nb_kde.5.50.5_n1_av<-c(0.74527557, NA, 0.94903253)
#nb_kde.5.50.5_n2_av<-c(0.58463283, NA, 0.9538141)
#nb_kde.5.50.5_n3_av<-c(0.7714502, NA, 0.86790697)
nb_kde.5.50.5_n4_av<-c(0.9449102, 0.9565835, 0.8965201)
#nb_kde.5.50.5_n5_av<-c(0.99798667, NA, NA)
######################## ROPE PCA
diff_nb_pca.5.50.5_n1_av<-nb_dataset_av - nb_pca.5.50.5_n1_av
bsr_diff_nb_pca.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.50.5_n1_av),-0.01,0.01)
bsr_diff_nb_pca.5.50.5_n1_av
## $winLeft
## [1] 0.6588667
##
## $winRope
## [1] 0.0618
##
## $winRight
## [1] 0.2793333
bsr_diff_nb_pca.5.50.5_n1_av_odds.left<-bsr_diff_nb_pca.5.50.5_n1_av$winLeft/bsr_diff_nb_pca.5.50.5_n1_av$winRight
bsr_diff_nb_pca.5.50.5_n1_av_odds.left
## [1] 2.358711
plot(rope(diff_nb_pca.5.50.5_n1_av,c(-0.01,0.01)))
#diff_nb_pca.5.50.5_n2_av<-nb_dataset_av - nb_pca.5.50.5_n2_av
#bsr_diff_nb_pca.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.50.5_n2_av),-0.01,0.01)
#bsr_diff_nb_pca.5.50.5_n2_av
#bsr_diff_nb_pca.5.50.5_n2_av_odds.left<-bsr_diff_nb_pca.5.50.5_n2_av$winLeft/bsr_diff_nb_pca.5.50.5_n2_av$winRight
#bsr_diff_nb_pca.5.50.5_n2_av_odds.left
#plot(rope(diff_nb_pca.5.50.5_n2_av,c(-0.01,0.01)))
#diff_nb_pca.5.50.5_n3_av<-nb_dataset_av - nb_pca.5.50.5_n3_av
#bsr_diff_nb_pca.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.50.5_n3_av),-0.01,0.01)
#bsr_diff_nb_pca.5.50.5_n3_av
#bsr_diff_nb_pca.5.50.5_n3_av_odds.left<-bsr_diff_nb_pca.5.50.5_n3_av$winLeft/bsr_diff_nb_pca.5.50.5_n3_av$winRight
#bsr_diff_nb_pca.5.50.5_n3_av_odds.left
#plot(rope(diff_nb_pca.5.50.5_n3_av,c(-0.01,0.01)))
diff_nb_pca.5.50.5_n4_av<-nb_dataset_av - nb_pca.5.50.5_n4_av
bsr_diff_nb_pca.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.50.5_n4_av),-0.01,0.01)
bsr_diff_nb_pca.5.50.5_n4_av
## $winLeft
## [1] 0.8
##
## $winRope
## [1] 0.0461
##
## $winRight
## [1] 0.1539
bsr_diff_nb_pca.5.50.5_n4_av_odds.left<-bsr_diff_nb_pca.5.50.5_n4_av$winLeft/bsr_diff_nb_pca.5.50.5_n4_av$winRight
bsr_diff_nb_pca.5.50.5_n4_av_odds.left
## [1] 5.198181
plot(rope(diff_nb_pca.5.50.5_n4_av,c(-0.01,0.01)))
#diff_nb_pca.5.50.5_n5_av<-nb_dataset_av - nb_pca.5.50.5_n5_av
#bsr_diff_nb_pca.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.50.5_n5_av),-0.01,0.01)
#bsr_diff_nb_pca.5.50.5_n5_av
#bsr_diff_nb_pca.5.50.5_n5_av_odds.left<-bsr_diff_nb_pca.5.50.5_n5_av$winLeft/bsr_diff_nb_pca.5.50.5_n5_av$winRight
#bsr_diff_nb_pca.5.50.5_n5_av_odds.left
#plot(rope(diff_nb_pca.5.50.5_n5_av,c(-0.01,0.01)))
########################## ROPE KDE
#diff_nb_kde.5.50.5_n1_av<-nb_dataset_av - nb_kde.5.50.5_n1_av
#bsr_diff_nb_kde.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.50.5_n1_av),-0.01,0.01)
#bsr_diff_nb_kde.5.50.5_n1_av
#bsr_diff_nb_kde.5.50.5_n1_av_odds.left<-bsr_diff_nb_kde.5.50.5_n1_av $winLeft/bsr_diff_nb_kde.5.50.5_n1_av$winRight
#bsr_diff_nb_kde.5.50.5_n1_av_odds.left
#plot(rope(diff_nb_kde.5.50.5_n1_av,c(-0.01,0.01)))
#diff_nb_kde.5.50.5_n2_av<-nb_dataset_av - nb_kde.5.50.5_n2_av
#bsr_diff_nb_kde.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.50.5_n2_av),-0.01,0.01)
#bsr_diff_nb_kde.5.50.5_n2_av
#bsr_diff_nb_kde.5.50.5_n2_av_odds.left<-bsr_diff_nb_kde.5.50.5_n2_av $winLeft/bsr_diff_nb_kde.5.50.5_n2_av$winRight
#bsr_diff_nb_kde.5.50.5_n2_av_odds.left
#plot(rope(diff_nb_kde.5.50.5_n2_av,c(-0.01,0.01)))
#diff_nb_kde.5.50.5_n3_av<-nb_dataset_av - nb_kde.5.50.5_n3_av
#bsr_diff_nb_kde.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.50.5_n3_av),-0.01,0.01)
#bsr_diff_nb_kde.5.50.5_n3_av
#bsr_diff_nb_kde.5.50.5_n3_av_odds.left<-bsr_diff_nb_kde.5.50.5_n3_av $winLeft/bsr_diff_nb_kde.5.50.5_n3_av$winRight
#bsr_diff_nb_kde.5.50.5_n3_av_odds.left
#plot(rope(diff_nb_kde.5.50.5_n3_av,c(-0.01,0.01)))
diff_nb_kde.5.50.5_n4_av<-nb_dataset_av - nb_kde.5.50.5_n4_av
bsr_diff_nb_kde.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.50.5_n4_av),-0.01,0.01)
bsr_diff_nb_kde.5.50.5_n4_av
## $winLeft
## [1] 0.8018
##
## $winRope
## [1] 0.04636667
##
## $winRight
## [1] 0.1518333
bsr_diff_nb_kde.5.50.5_n4_av_odds.left<-bsr_diff_nb_kde.5.50.5_n4_av $winLeft/bsr_diff_nb_kde.5.50.5_n4_av$winRight
bsr_diff_nb_kde.5.50.5_n4_av_odds.left
## [1] 5.28079
plot(rope(diff_nb_kde.5.50.5_n4_av,c(-0.01,0.01)))
#diff_nb_kde.5.50.5_n5_av<-nb_dataset_av - nb_kde.5.50.5_n5_av
#bsr_diff_nb_kde.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.50.5_n5_av),-0.01,0.01)
#bsr_diff_nb_kde.5.50.5_n5_av
#bsr_diff_nb_kde.5.50.5_n5_av_odds.left<-bsr_diff_nb_kde.5.50.5_n5_av $winLeft/bsr_diff_nb_kde.5.50.5_n5_av$winRight
#bsr_diff_nb_kde.5.50.5_n5_av_odds.left
#plot(rope(diff_nb_kde.5.50.5_n5_av,c(-0.01,0.01)))